{"id":"73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3","title":"Yusheng Zhao and coauthors introduce SciGame for testing scientific discovery agents","argument":"Yusheng Zhao, Bowei He, Xue Liu and Mingxiao Li introduce SciGame, a benchmark for testing how language-model agents investigate scientific systems and infer their governing laws. They argue that existing benchmarks can blur law inference with programming, reward fitting known data rather than prediction on unseen inputs, and fail to separate empirical evidence from pretrained priors. SciGame turns scientific literature into structured scientific cards, each paired with Original and dimensionally consistent Counterfactual environments. The authors test agents in Identification and Discovery settings, using Prior-only, Fixed and Adaptive protocols. In experiments on five frontier models, they report that familiar priors can hurt when a relation changes, and that adaptive experimentation helps stronger models but harms weaker ones under limited budgets. They also find that good explanations can conceal quantitative errors. The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly. They conclude that agents can actively investigate systems, but exact law induction in unfamiliar settings remains an open challenge.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence is the paper's abstract, not its full methods or results; it does not identify the five frontier models or provide detailed experimental measurements. The abstract reports benchmark findings but does not establish how well these results predict agents' performance in real scientific research.","speaker":{"name":"Zhao, Yusheng, He, Bowei, Liu, Xue, Li, Mingxiao","roleAtSource":"Article author"},"source":{"title":"SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.37919","publishedAt":"2026-10-27","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors introduce SciGame to evaluate agents' scientific discovery in paired Original and Counterfactual environments.","excerpt":"We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors say existing benchmarks mix law inference with programming, assess data fitting rather than prediction on unseen inputs, and do not separate evidence from pretrained priors.","excerpt":"existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"In experiments on five frontier models, the authors report that familiar priors hurt when the governing relation is altered, while adaptive experimentation has different effects on stronger and weaker models under limited budgets.","excerpt":"familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly.","excerpt":"Recovering the exact symbolic form remains the main bottleneck, even when agents interpret evidence correctly.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors conclude that current agents can investigate systems actively, but exact law induction in unfamiliar settings remains unresolved.","excerpt":"Current agents can investigate systems actively, but inducing exact laws in unfamiliar settings remains an open challenge.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.37919","checkedAt":"2026-11-11T06:24:07.391Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How can agents recover the exact symbolic form when they interpret evidence correctly?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Yusheng Zhao and coauthors introduce SciGame for testing scientific discovery agents","summary":"Yusheng Zhao, Bowei He, Xue Liu and Mingxiao Li introduce SciGame, a benchmark for testing how language-model agents investigate scientific systems and infer their governing laws. They argue that existing benchmarks can blur law inference with programming, reward fitting known data rather than prediction on unseen inputs, and fail to separate empirical evidence from pretrained priors. SciGame turns scientific literature into structured scientific cards, each paired with Original and dimensionally consistent Counterfactual environments. The authors test agents in Identification and Discovery settings, using Prior-only, Fixed and Adaptive protocols. In experiments on five frontier models, they report that familiar priors can hurt when a relation changes, and that adaptive experimentation helps stronger models but harms weaker ones under limited budgets. They also find that good explanations can conceal quantitative errors. The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly. They conclude that agents can actively investigate systems, but exact law induction in unfamiliar settings remains an open challenge.","question":"How can agents recover the exact symbolic form when they interpret evidence correctly?","claims":[{"text":"The authors introduce SciGame to evaluate agents' scientific discovery in paired Original and Counterfactual environments.","excerpt":"We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards.","support":"source-stated"},{"text":"The authors say existing benchmarks mix law inference with programming, assess data fitting rather than prediction on unseen inputs, and do not separate evidence from pretrained priors.","excerpt":"existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors.","support":"source-stated"},{"text":"In experiments on five frontier models, the authors report that familiar priors hurt when the governing relation is altered, while adaptive experimentation has different effects on stronger and weaker models under limited budgets.","excerpt":"familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets.","support":"source-stated"},{"text":"The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly.","excerpt":"Recovering the exact symbolic form remains the main bottleneck, even when agents interpret evidence correctly.","support":"source-stated"},{"text":"The authors conclude that current agents can investigate systems actively, but exact law induction in unfamiliar settings remains unresolved.","excerpt":"Current agents can investigate systems actively, but inducing exact laws in unfamiliar settings remains an open challenge.","support":"source-stated"}],"limitations":["The supplied evidence is the paper's abstract, not its full methods or results; it does not identify the five frontier models or provide detailed experimental measurements.","The abstract reports benchmark findings but does not establish how well these results predict agents' performance in real scientific research."],"mediaId":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","correctionReason":null,"id":"73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3","publicationId":"3cd323f5-039b-4ce3-8c69-32aad0e76d8f","version":1,"sourceUrl":"https://arxiv.org/abs/2610.37919","sourceTitle":"SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-27","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-30T06:45:35.195Z","createdAt":"2026-11-11T05:53:57.599Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","kind":"article","url":"https://arxiv.org/abs/2610.37919","sourceUrl":"https://arxiv.org/abs/2610.37919","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-31T07:33:04.771Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.37919","checkedAt":"2026-11-11T06:24:07.391Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","confidence":0.78,"provider":"typesafe","model":"jev-1.13.0","inputHash":"0d3924be0b4874ee96e790537e628e8b57de34fb3e56309ed91a9a8aaa954b47","decidedAt":"2026-10-31T07:33:06.439Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observationId":"40f5e45588a8ebc5eaad579d273edcd3faf0061cd67a4cdcb0889850ec00efec","retrievedAt":"2026-10-31T07:33:04.525Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"c5957411-feb3-4c29-815e-252cca1d8ebe"},"reviewer":{"model":"gpt-6-luna","runId":"576b67ca-2fa1-462e-83ad-16f06445de70"}},"audit":{"status":"passed","reviewedAt":"2026-11-11T05:53:57.599Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Zhao, Yusheng","url":null,"kind":"unknown"},{"name":"He, Bowei","url":null,"kind":"unknown"},{"name":"Liu, Xue","url":null,"kind":"unknown"},{"name":"Li, Mingxiao","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Zhao, Yusheng","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhao, Yusheng","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"He, Bowei","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"He, Bowei","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"Liu, Xue","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Liu, Xue","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"Li, Mingxiao","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Mingxiao","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}}],"participationCoverage":{"total":4,"returned":4,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.37919","snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z","basis":"publisher-page-metadata","sourceLinks":43,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3/source"}},"canonicalUrl":"https://escapemeridian.com/readings/yusheng-zhao-and-coauthors-introduce-scigame-for-testing-73cab748","formats":{"html":"/readings/yusheng-zhao-and-coauthors-introduce-scigame-for-testing-73cab748","markdown":"/readings/yusheng-zhao-and-coauthors-introduce-scigame-for-testing-73cab748.md","json":"/readings/yusheng-zhao-and-coauthors-introduce-scigame-for-testing-73cab748.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-benchmark","name":"Benchmark","explanation":"A defined set of tests used to compare systems. Results depend on the tasks and measures selected.","referenceUrl":"https://arxiv.org/abs/2211.09110"},{"id":"term-pre-training","name":"Pre-training","explanation":"A model's initial training on a large dataset, before further adaptation for particular uses.","referenceUrl":"https://developers.google.com/machine-learning/glossary#pre-training"},{"id":"term-counterfactual","name":"Counterfactual","explanation":"An argument or scenario that considers what would have resulted if events had gone differently from how they actually did, such as if a policy had never been adopted; as an adjective, relating to this kind of thinking about what did not happen but could have.","referenceUrl":"https://dictionary.cambridge.org/dictionary/english/counterfactual"},{"id":"term-frontier-model","name":"Frontier model","explanation":"One of the most capable AI models available at a given time; the set shifts as models improve.","referenceUrl":"https://www.gov.uk/government/publications/frontier-ai-capabilities-and-risks-discussion-paper"}],"entities":[{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3","revision":"3cd323f5-039b-4ce3-8c69-32aad0e76d8f","revisionKind":"publication-id","title":"Yusheng Zhao and coauthors introduce SciGame for testing scientific discovery agents","url":"https://escapemeridian.com/readings/yusheng-zhao-and-coauthors-introduce-scigame-for-testing-73cab748"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.37919","revision":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","revisionKind":"sha256-source-text","title":"SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World","url":"https://arxiv.org/abs/2610.37919"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3","title":"Yusheng Zhao and coauthors introduce SciGame for testing scientific discovery agents","argument":"Yusheng Zhao, Bowei He, Xue Liu and Mingxiao Li introduce SciGame, a benchmark for testing how language-model agents investigate scientific systems and infer their governing laws. They argue that existing benchmarks can blur law inference with programming, reward fitting known data rather than prediction on unseen inputs, and fail to separate empirical evidence from pretrained priors. SciGame turns scientific literature into structured scientific cards, each paired with Original and dimensionally consistent Counterfactual environments. The authors test agents in Identification and Discovery settings, using Prior-only, Fixed and Adaptive protocols. In experiments on five frontier models, they report that familiar priors can hurt when a relation changes, and that adaptive experimentation helps stronger models but harms weaker ones under limited budgets. They also find that good explanations can conceal quantitative errors. The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly. They conclude that agents can actively investigate systems, but exact law induction in unfamiliar settings remains an open challenge.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence is the paper's abstract, not its full methods or results; it does not identify the five frontier models or provide detailed experimental measurements. The abstract reports benchmark findings but does not establish how well these results predict agents' performance in real scientific research.","speaker":{"name":"Zhao, Yusheng, He, Bowei, Liu, Xue, Li, Mingxiao","roleAtSource":"Article author"},"source":{"title":"SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.37919","publishedAt":"2026-10-27","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors introduce SciGame to evaluate agents' scientific discovery in paired Original and Counterfactual environments.","excerpt":"We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors say existing benchmarks mix law inference with programming, assess data fitting rather than prediction on unseen inputs, and do not separate evidence from pretrained priors.","excerpt":"existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"In experiments on five frontier models, the authors report that familiar priors hurt when the governing relation is altered, while adaptive experimentation has different effects on stronger and weaker models under limited budgets.","excerpt":"familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly.","excerpt":"Recovering the exact symbolic form remains the main bottleneck, even when agents interpret evidence correctly.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"},{"text":"The authors conclude that current agents can investigate systems actively, but exact law induction in unfamiliar settings remains unresolved.","excerpt":"Current agents can investigate systems actively, but inducing exact laws in unfamiliar settings remains an open challenge.","url":"https://arxiv.org/abs/2610.37919","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.37919","checkedAt":"2026-11-11T06:24:07.391Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How can agents recover the exact symbolic form when they interpret evidence correctly?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Yusheng Zhao and coauthors introduce SciGame for testing scientific discovery agents","summary":"Yusheng Zhao, Bowei He, Xue Liu and Mingxiao Li introduce SciGame, a benchmark for testing how language-model agents investigate scientific systems and infer their governing laws. They argue that existing benchmarks can blur law inference with programming, reward fitting known data rather than prediction on unseen inputs, and fail to separate empirical evidence from pretrained priors. SciGame turns scientific literature into structured scientific cards, each paired with Original and dimensionally consistent Counterfactual environments. The authors test agents in Identification and Discovery settings, using Prior-only, Fixed and Adaptive protocols. In experiments on five frontier models, they report that familiar priors can hurt when a relation changes, and that adaptive experimentation helps stronger models but harms weaker ones under limited budgets. They also find that good explanations can conceal quantitative errors. The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly. They conclude that agents can actively investigate systems, but exact law induction in unfamiliar settings remains an open challenge.","question":"How can agents recover the exact symbolic form when they interpret evidence correctly?","claims":[{"text":"The authors introduce SciGame to evaluate agents' scientific discovery in paired Original and Counterfactual environments.","excerpt":"We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards.","support":"source-stated"},{"text":"The authors say existing benchmarks mix law inference with programming, assess data fitting rather than prediction on unseen inputs, and do not separate evidence from pretrained priors.","excerpt":"existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors.","support":"source-stated"},{"text":"In experiments on five frontier models, the authors report that familiar priors hurt when the governing relation is altered, while adaptive experimentation has different effects on stronger and weaker models under limited budgets.","excerpt":"familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets.","support":"source-stated"},{"text":"The authors identify recovering the exact symbolic form as the main bottleneck, even when agents interpret evidence correctly.","excerpt":"Recovering the exact symbolic form remains the main bottleneck, even when agents interpret evidence correctly.","support":"source-stated"},{"text":"The authors conclude that current agents can investigate systems actively, but exact law induction in unfamiliar settings remains unresolved.","excerpt":"Current agents can investigate systems actively, but inducing exact laws in unfamiliar settings remains an open challenge.","support":"source-stated"}],"limitations":["The supplied evidence is the paper's abstract, not its full methods or results; it does not identify the five frontier models or provide detailed experimental measurements.","The abstract reports benchmark findings but does not establish how well these results predict agents' performance in real scientific research."],"mediaId":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","correctionReason":null,"id":"73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3","publicationId":"3cd323f5-039b-4ce3-8c69-32aad0e76d8f","version":1,"sourceUrl":"https://arxiv.org/abs/2610.37919","sourceTitle":"SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-27","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-30T06:45:35.195Z","createdAt":"2026-11-11T05:53:57.599Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","kind":"article","url":"https://arxiv.org/abs/2610.37919","sourceUrl":"https://arxiv.org/abs/2610.37919","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-31T07:33:04.771Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.37919","checkedAt":"2026-11-11T06:24:07.391Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"91b3edf2f063b8746a4f5293c66f55f759ad336c1720bfa82c22426a74df35db","confidence":0.78,"provider":"typesafe","model":"jev-1.13.0","inputHash":"0d3924be0b4874ee96e790537e628e8b57de34fb3e56309ed91a9a8aaa954b47","decidedAt":"2026-10-31T07:33:06.439Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observationId":"40f5e45588a8ebc5eaad579d273edcd3faf0061cd67a4cdcb0889850ec00efec","retrievedAt":"2026-10-31T07:33:04.525Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"c5957411-feb3-4c29-815e-252cca1d8ebe"},"reviewer":{"model":"gpt-6-luna","runId":"576b67ca-2fa1-462e-83ad-16f06445de70"}},"audit":{"status":"passed","reviewedAt":"2026-11-11T05:53:57.599Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Zhao, Yusheng","url":null,"kind":"unknown"},{"name":"He, Bowei","url":null,"kind":"unknown"},{"name":"Liu, Xue","url":null,"kind":"unknown"},{"name":"Li, Mingxiao","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Zhao, Yusheng","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhao, Yusheng","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"He, Bowei","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"He, Bowei","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"Liu, Xue","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Liu, Xue","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}},{"actor":{"name":"Li, Mingxiao","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Mingxiao","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.37919","locator":"head/meta[name=author|citation_author]"},"assertionId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T06:24:07.391Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","sourceUrl":"https://arxiv.org/abs/2610.37919","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z"}}],"participationCoverage":{"total":4,"returned":4,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.37919","snapshotId":"5d56931a7f598cab733aca91f0201a53fe5a754c10c3dd77f5505118127cf54c","contentSha256":"140af275c3f6e7fe87f9026e032ca7919637567974e4b06f179bad551fa838a8","textHash":"937dcccb398dd8c115fa15769e6407a127ef93bc9e3cace754d64c28776b49a5","observedAt":"2026-11-11T06:24:07.391Z","basis":"publisher-page-metadata","sourceLinks":43,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/73cab748e4254d761f874e43480dad60394c5e8b06ef3d04498917d6018f7cc3/source"}}}],"audits":[{"publicationId":"3cd323f5-039b-4ce3-8c69-32aad0e76d8f","verdict":"passed","findings":[{"dimension":"attribution","status":"pass","explanation":"The draft correctly credits the benchmark and findings to the four listed authors, Yusheng Zhao, Bowei He, Xue Liu and Mingxiao Li. It frames the results as their reported findings, not as established fact.","excerpt":"Authors:Yusheng Zhao, Bowei He, Xue Liu, Mingxiao Li"},{"dimension":"summary","status":"pass","explanation":"The title, summary and question faithfully follow the abstract: the critique of benchmarks, the cards with Original and Counterfactual environments, the settings and protocols, the three findings, the bottleneck and the conclusion. Terms such as pretrained priors, Counterfactual, symbolic form and frontier models are kept exactly, suitable for tooltips. The phrase about priors hurting when a relation changes gives lay readers the essential idea. The Temporal collection of post-cutoff papers is omitted, but this does not distort meaning. The question follows from the stated bottleneck.","excerpt":"Recovering the exact symbolic form remains the main bottleneck, even when agents interpret evidence correctly."},{"dimension":"date","status":"pass","explanation":"originalPublished 2026-10-27 matches the v1 submission date on arXiv. It is not the later feed date of 2026-10-31.","excerpt":"[Submitted on 27 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim and summary statement is directly supported by a verbatim abstract excerpt. Hedges such as can hurt and can conceal stay within the source. The limitations correctly note that only the abstract is available, that the models are unnamed, and that real-world transfer is not established. No benefits or costs are invented.","excerpt":"Third, good explanations can hide quantitative failures."},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper. It is the most direct way to access the source.","excerpt":null}],"createdAt":"2026-11-11T05:53:57.599Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}